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cs.LG2026
Latent Space Inference via Paired Autoencoders
Emma Hart, Bas Peters, Julianne Chung +1
This work describes a novel data-driven latent space inference framework built on paired autoencoders to handle observational inconsistencies when solving inverse problems. Our app…
cs.LG2025
Optimal Linear Baseline Models for Scientific Machine Learning
Alexander DeLise, Kyle Loh, Krish Patel +3
Across scientific domains, a fundamental challenge is to characterize and compute the mappings from underlying physical processes to observed signals and measurements. While nonlin…
cs.LG2025
Good Things Come in Pairs: Paired Autoencoders for Inverse Problems
Matthias Chung, Bas Peters, Michael Solomon
In this book chapter, we discuss recent advances in data-driven approaches for inverse problems. In particular, we focus on the \emph{paired autoencoder} framework, which has prove…